Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	19,009
2	New Jersey	17,633
3	Massachusetts	13,669
4	Rhode Island	13,548
5	District of Columbia	11,910
6	Connecticut	11,580
7	Delaware	9,341
8	Illinois	9,044
9	Louisiana	8,323
10	Maryland	8,111
11	Nebraska	6,707
12	Iowa	5,846
13	Pennsylvania	5,752
14	Michigan	5,561
15	South Dakota	5,324
16	Indiana	4,937
17	Mississippi	4,718
18	Virginia	4,715
19	Colorado	4,302
20	Georgia	4,022
21	Minnesota	3,985
22	New Mexico	3,458
23	Alabama	3,269
24	Kansas	3,244
25	North Dakota	3,204
26	New Hampshire	3,152
27	Tennessee	3,127
28	Ohio	2,860
29	Wisconsin	2,845
30	Washington	2,831
31	Utah	2,716
32	Nevada	2,645
33	California	2,578
34	Florida	2,450
35	Arizona	2,371
36	North Carolina	2,363
37	Kentucky	2,084
38	Arkansas	2,079
39	South Carolina	2,063
40	Missouri	2,056
41	Texas	2,036
42	Maine	1,589
43	Oklahoma	1,574
44	Vermont	1,556
45	Idaho	1,528
46	Wyoming	1,485
47	West Virginia	1,086
48	Puerto Rico	1,063
49	Oregon	957
50	Alaska	570
51	Montana	450
52	Hawaii	447

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Virginia	156
2	Nebraska	145
3	District of Columbia	139
4	Maryland	117
5	Minnesota	111
6	Illinois	107
7	Iowa	106
8	Alabama	105
9	Louisiana	100
10	Delaware	98
11	New Jersey	92
12	Rhode Island	90
13	Mississippi	88
14	Connecticut	76
15	Massachusetts	74
16	Wisconsin	68
17	California	60
18	Tennessee	60
19	New York	59
20	South Dakota	55
21	Georgia	54
22	Indiana	54
23	Pennsylvania	51
24	New Mexico	49
25	North Carolina	49
26	Ohio	43
27	Arizona	42
28	Arkansas	39
29	Colorado	36
30	Kansas	36
31	Washington	36
32	Kentucky	35
33	South Carolina	34
34	Texas	34
35	New Hampshire	33
36	Utah	33
37	West Virginia	32
38	Michigan	30
39	Nevada	28
40	Florida	27
41	Missouri	27
42	Wyoming	27
43	Puerto Rico	21
44	Maine	20
45	Oklahoma	16
46	Idaho	15
47	North Dakota	10
48	Oregon	8
49	Vermont	8
50	Alaska	3
51	Hawaii	0
52	Montana	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,508
2	New Jersey	1,276
3	Connecticut	1,066
4	Massachusetts	949
5	District of Columbia	630
6	Rhode Island	618
7	Louisiana	585
8	Michigan	534
9	Pennsylvania	413
10	Illinois	403
11	Maryland	395
12	Delaware	353
13	Indiana	301
14	Colorado	241
15	Mississippi	225
16	Georgia	179
17	Ohio	174
18	Minnesota	167
19	New Hampshire	164
20	Iowa	157
21	New Mexico	156
22	Virginia	150
23	Washington	145
24	Nevada	130
25	Alabama	118
26	Arizona	114
27	Missouri	114
28	Florida	107
29	California	100
30	Kentucky	94
31	Wisconsin	92
32	South Carolina	90
33	Nebraska	86
34	Vermont	86
35	Oklahoma	81
36	North Carolina	78
37	North Dakota	77
38	Kansas	71
39	South Dakota	61
40	Maine	60
41	Texas	55
42	Tennessee	51
43	Idaho	45
44	West Virginia	41
45	Puerto Rico	40
46	Arkansas	39
47	Oregon	35
48	Utah	32
49	Wyoming	24
50	Montana	15
51	Hawaii	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Rhode Island	14
2	Connecticut	10
3	Massachusetts	8
4	New Jersey	7
5	Delaware	6
6	District of Columbia	6
7	Illinois	6
8	Maryland	6
9	Mississippi	5
10	New York	5
11	Iowa	4
12	Virginia	4
13	Colorado	3
14	Georgia	3
15	Michigan	3
16	Minnesota	3
17	New Hampshire	3
18	Pennsylvania	3
19	Alabama	2
20	Indiana	2
21	Louisiana	2
22	Nebraska	2
23	North Dakota	2
24	Ohio	2
25	South Carolina	2
26	Arizona	1
27	California	1
28	Florida	1
29	Kentucky	1
30	New Mexico	1
31	North Carolina	1
32	South Dakota	1
33	Tennessee	1
34	Washington	1
35	Wisconsin	1
36	Wyoming	1
37	Alaska	0
38	Arkansas	0
39	Hawaii	0
40	Idaho	0
41	Kansas	0
42	Maine	0
43	Missouri	0
44	Montana	0
45	Nevada	0
46	Oklahoma	0
47	Oregon	0
48	Puerto Rico	0
49	Texas	0
50	Utah	0
51	Vermont	0
52	West Virginia	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	123,361	1	99
Dakota	Nebraska	82,343	2	99
Lincoln	Arkansas	74,862	3	99
Nobles	Minnesota	68,797	4	99
Lake	Tennessee	58,865	5	99
Davidson	Tennessee	7,301	258	91
Richland	South Carolina	3,379	681	78
Pierce	Washington	2,098	1016	67
Orange	California	1,779	1162	63
York	South Carolina	1,292	1455	53

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Terrell	Georgia	3,048	1	99
Early	Georgia	2,944	2	99
Randolph	Georgia	2,803	3	99
Hancock	Georgia	2,601	4	99
Essex	New Jersey	2,033	5	99
Richland	South Carolina	159	568	81
Davidson	Tennessee	86	830	73
Pierce	Washington	84	841	73
Orange	California	43	1208	61
York	South Carolina	21	1488	52

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.